Evaluation of Multiclass Traffic Sign Detection and Classification Methods for U.S. Roadway Asset Inventory Management

Detection, classification, and mapping of U.S. traffic signs using google street view images for roadway inventory management

“…We briefly describe this method and modifications in method section. More detailed information on available techniques can be found in (Balali and Golparvar-Fard 2015a). One missing thread is that the scalability of these methods.…”

“…More recent studies such as (Balali and Golparvar-Fard Using an integrated GPS/GIS field data logger to record and store inventory information (Caddell et al 2009;Jones 2004) Aerial/Satellite photography Analyzing high resolution images taken from aircraft or satellites to identify and extract highway inventory information (Veneziano et al 2002) (Balali and Golparvar-Fard 2014;Prisacariu et al 2010). Support Vector Machines (SVM) (I. M. Creusen et al 2010;Jahangiri and Rakha 2014;Xie et al 2009), neural networks, and cascaded classifiers trained with some type of boosting (Balali and Golparvar-Fard 2015a;Overett et al 2014;Pettersson et al 2008) are used for classification of traffic signs. (Balali and Golparvar-Fard 2015a) benchmarked and compared the performance of the most relevant methods.…”

“…Support Vector Machines (SVM) (I. M. Creusen et al 2010;Jahangiri and Rakha 2014;Xie et al 2009), neural networks, and cascaded classifiers trained with some type of boosting (Balali and Golparvar-Fard 2015a;Overett et al 2014;Pettersson et al 2008) are used for classification of traffic signs. (Balali and Golparvar-Fard 2015a) benchmarked and compared the performance of the most relevant methods. Using a large visual dataset of traffic signs and their ground truth, they showed that the joint representation of texture and color in HOG + Color histograms with multiple linear SVM classifiers result in the best performance for classification of multiple categorizes of traffic signs.…”

“…In this paper we use HOG + Color with linear SVM classifier for detection and classification traffic signs since (Balali and Golparvar-Fard 2015a) showed this method has the best performance. Different from the state-ofthe-art methods (Stallkamp et al 2011;Tsai et al 2009), we do not make any prior assumption on the 2D location of traffic signs in the images.…”

“…For evaluating the performance of proposed method, the multi-class traffic sign detection model of (Balali and Golparvar-Fard 2015a) was trained using regular images collected from a highway and many secondary roadways in the U.S. This dataset -shown in Table 4contains different categories of traffic signs based on the messages they communicate.…”